[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-202199-105":59,"doc-detail-202199-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-sparse-random-projection-based-test-for-overall-qualitative-treatment-effects","A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects","","A sparse random projection-based testing framework evaluates overall qualitative treatment effects (OQTE) within precision medicine settings. The core hypothesis links OQTE to whether implementing the optimal individualized treatment regime (ITR) is equivalent to using a one-size-fits-all strategy. The document outlines key estimation approaches for the optimal ITR, including Q-learning, A-learning, value search, outcome weighted learning, decision lists, and tree-based methods, and then specifies clinical-trial examples used for illustration and validation.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-sparse-random-projection-based-test-for-overall-qualitative-treatment-effects/202199/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-sparse-random-projection-based-test-for-overall-qualitative-treatment-effects/202199.png","ImageObject",300,407,{"name":92,"@type":93},"Noah","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-04",true,{"@type":102,"interactionType":103,"userInteractionCount":52},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does the OQTE hypothesis test compare in the document?","Question",{"text":112,"@type":113},"It tests whether there are no overall qualitative treatment effects, which is equivalent to the optimal ITR producing the same recommendation as a one-size-fits-all approach.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"Which methods are presented for estimating the optimal ITR?",{"text":117,"@type":113},"The document lists Q-learning, A-learning, value search, outcome weighted learning, decision lists, and tree-based methods.",{"name":119,"@type":110,"acceptedAnswer":120},"How are the clinical trial studies used in the material?",{"text":121,"@type":113},"Nefazodone-CBASP and STAR*D studies are described with response measures and available covariates to support the OQTE testing and individualized regime estimation setup.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},202199,1788543487,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":52,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":129,"read_time":144,"preview_page_count":4},8796095462418,"https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780","A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects  \nChengchun Shi  \nDepartment of Statistics North Carolina State University  \nJoint work with Wenbin Lu and Rui Song, JASA, accepted  \n Chengchun Shi (NCSU)  OQTE  1 / 22   \nOne-size-fits-all  \nxxxx xxxx  \nxxxx  \nPatients  \nChengchun Shi (NCSU)  OQTE  2 / 22   \nOne-size-fits-all  \nxxxx xxxx  \nxxxx  \nPatients  \nChengchun Shi (NCSU)  OQTE  2 / 22   \nPrecision medicine: individualized treatment regime (ITR)  \nxxxx xxxx  \nxxxx  \nPatients  \nChengchun Shi (NCSU)  OQTE  3 / 22   \nEstimation of the optimal ITR  \n Q-learning (Watkins and Dayan, 1992; Murphy, 2005; Moodie et al. , 2014; Song et al., 2015)  \n A-learning (Murphy, 2003; Robins, 2004; Schulte et al. , 2014)  Value search method (Zhang et al. , 2012, 2013)  \n Outcome weighted learning (OWL, Zhao et al. , 2012, 2015)  \n Decision lists (Zhang et al. , 2015, 2017)  \n Tree-based methods (Laber and Zhao, 2015; Zhu et al. , 2017)  \nInference of the optimal ITR  \n All the above estimation methods implicitly assume patients’covariates have qualitative interactions with the treatment  \n Testing the overall qualitative treatment effects (OQTE) :  \nH0 : No OQTE ⇐⇒ Implementing the optimal ITR is the same as“one-size-fits-all”.  \n Chengchun Shi (NCSU)  OQTE  4 / 22   \nNefazodone-CBASP clinical trial study  \n 681 patients with nonpsychotic chronic major depressive disorder  \n Treatments:  \n Nefazodone;  \n Cognitive Behavioral-Analysis System of Psychotherapy (CBASP);  the combination of Nefazodone and CBASP.  \n Response: the negative 24-item Hamilton Rating Scale for Depression (HRSD) .  \n Covariates: 647 patients with complete records of 50 covariates.  \n Chengchun Shi (NCSU)  OQTE  6 / 22   \nSequenced Treatment Alternatives to Relieve Depressions (STAR*D) Study  \n381 covariates available at Level 3, 305 at Level 2  \n 73 patients BUP or SER at Level 2, MRT or NTP at Level 3  \n Chengchun Shi (NCSU)  OQTE  7 / 22   \nIndividualized treatment regime (ITR) : a function maps the covariate space to the space of available treatment options.  \n Optimal ITR: yields the most favorable clinical outcome.  Single stage study:  \n Covariates X: p-dimensional vector.  \n Treatment A: 1 for the treatment, 0 for the control.  \n Response Y: assuming larger values are desirable.  Q-function: Q (a ; x) = E(Y |A = a ; X = x) .  \n Contrast function: 􀀜 (x) = Q(1; x) − Q(0; x) .  \n Chengchun Shi (NCSU)  OQTE  8 / 22   \nIndividualized treatment regime (ITR) : a function maps the covariate space to the space of available treatment options.  \n Optimal ITR: yields the most favorable clinical outcome.  Single stage study:  \n Covariates X: p-dimensional vector.  \n Treatment A: 1 for the treatment, 0 for the control.  \n Response Y: assuming larger values are desirable.  Q-function: Q (a ; x) = E(Y |A = a ; X = x) .  \n Contrast function: 􀀜 (x) = Q(1; x) − Q(0; x) .  \n􀀜 (x) > 0  \n Chengchun Shi (NCSU)  OQTE  8 / 22","cbCaincGZVOvIaN9","https://ap.wps.com/l/cbCaincGZVOvIaN9","pdf",193588,35,"English","# Estimation of the optimal ITR\n## Q-learning and A-learning\n## Value search, OWL, decision lists, and tree-based methods\n# Testing the overall qualitative treatment effects (OQTE)\n## Hypothesis connecting OQTE to one-size-fits-all\n# Clinical trial study examples\n## Nefazodone-CBASP trial\n## STAR*D study\n# Individualized treatment regime (ITR) setup\n## Q-function and contrast function","[{\"question\":\"What does the OQTE hypothesis test compare in the document?\",\"answer\":\"It tests whether there are no overall qualitative treatment effects, which is equivalent to the optimal ITR producing the same recommendation as a one-size-fits-all approach.\"},{\"question\":\"Which methods are presented for estimating the optimal ITR?\",\"answer\":\"The document lists Q-learning, A-learning, value search, outcome weighted learning, decision lists, and tree-based methods.\"},{\"question\":\"How are the clinical trial studies used in the material?\",\"answer\":\"Nefazodone-CBASP and STAR*D studies are described with response measures and available covariates to support the OQTE testing and individualized regime estimation setup.\"}]","A Sparse Random Projection-based Test for Overall Qualitative Treatment Effects | PDF",88]